4 research outputs found
Robust Classification under Class-Dependent Domain Shift
Investigation of machine learning algorithms robust to changes between the
training and test distributions is an active area of research. In this paper we
explore a special type of dataset shift which we call class-dependent domain
shift. It is characterized by the following features: the input data causally
depends on the label, the shift in the data is fully explained by a known
variable, the variable which controls the shift can depend on the label, there
is no shift in the label distribution. We define a simple optimization problem
with an information theoretic constraint and attempt to solve it with neural
networks. Experiments on a toy dataset demonstrate the proposed method is able
to learn robust classifiers which generalize well to unseen domains.Comment: Accepted at ICML 2020 workshop on Uncertainty and Robustness in Deep
Learnin
W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos
Deep Neural Networks are increasingly used in video frame interpolation tasks
such as frame rate changes as well as generating fake face videos. Our project
aims to apply recent advances in Deep video interpolation to increase the
temporal resolution of fluorescent microscopy time-lapse movies. To our
knowledge, there is no previous work that uses Convolutional Neural Networks
(CNN) to generate frames between two consecutive microscopy images. We propose
a fully convolutional autoencoder network that takes as input two images and
generates upto seven intermediate images. Our architecture has two encoders
each with a skip connection to a single decoder. We evaluate the performance of
several variants of our model that differ in network architecture and loss
function. Our best model out-performs state of the art video frame
interpolation algorithms. We also show qualitative and quantitative comparisons
with state-of-the-art video frame interpolation algorithms. We believe deep
video interpolation represents a new approach to improve the time-resolution of
fluorescent microscopy
Learn what you can't learn: Regularized Ensembles for Transductive Out-of-distribution Detection
Machine learning models are often used in practice if they achieve good
generalization results on in-distribution (ID) holdout data. When employed in
the wild, they should also be able to detect samples they cannot predict well.
We show that current out-of-distribution (OOD) detection algorithms for neural
networks produce unsatisfactory results in a variety of OOD detection
scenarios, e.g. when OOD data consists of unseen classes or corrupted
measurements. This paper studies how such "hard" OOD scenarios can benefit from
adjusting the detection method after observing a batch of the test data. This
transductive setting is relevant when the advantage of even a slightly delayed
OOD detection outweighs the financial cost for additional tuning. We propose a
novel method that uses an artificial labeling scheme for the test data and
regularization to obtain ensembles of models that produce contradictory
predictions only on the OOD samples in a test batch. We show via comprehensive
experiments that our approach is indeed able to significantly outperform both
inductive and transductive baselines on difficult OOD detection scenarios, such
as unseen classes on CIFAR-10/CIFAR-100, severe corruptions(CIFAR-C), and
strong covariate shift (ImageNet vs ObjectNet)
Robust Learning with the Hilbert-Schmidt Independence Criterion
We investigate the use of a non-parametric independence measure, the
Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning
robust regression and classification models. This loss-function encourages
learning models where the distribution of the residuals between the label and
the model prediction is statistically independent of the distribution of the
instances themselves. This loss-function was first proposed by Mooij et al.
(2009) in the context of learning causal graphs. We adapt it to the task of
learning for unsupervised covariate shift: learning on a source domain without
access to any instances or labels from the unknown target domain, but with the
assumption that (the conditional probability of labels given
instances) remains the same in the target domain. We show that the proposed
loss is expected to give rise to models that generalize well on a class of
target domains characterised by the complexity of their description within a
reproducing kernel Hilbert space. Experiments on unsupervised covariate shift
tasks demonstrate that models learned with the proposed loss-function
outperform models learned with standard loss functions, achieving
state-of-the-art results on a challenging cell-microscopy unsupervised
covariate shift task.Comment: Proceedings of the 37th International Conference on Machine Learning
(ICML 2020